Bionic Turtle’s Week in Risk (ending January 20th)

Welcome to our Week in Risk blog! David recorded two new YouTube videos discussing the binomial tree option price: American-style and Foreign exchange exposure for the unhedged balance sheet. You can test your FRM knowledge with our newest daily practice questions also! Our most recent practice questions discuss swap rates versus spot rates and big data techniques including machine learning. We hope you have a great week!

New YouTube

New Practice Questions

Swap rates versus spot rates

In the Forum

  • [P1.T1] Sample interview questions about the capital asset pricing model (CAPM)  https://trtl.bz/2FENm6Z
  • [P1.T2] Diebold EOC questions are not exam representative https://trtl.bz/2FDDXN2
  • [P1.T2] In most case, the null hypothesis can be deduced from a minimum setup (a key tip is that the null must contain the equality!)  https://trtl.bz/2FE1CfZ

chi-squared

Basel IMA backtest

  • [P2.T5] In a value at risk (Var) backtest, the sample failure rate is an unbiased estimator  https://trtl.bz/2FvsPAI
  • [P2.T5] When an alternative swap duration is not really different than our approach  https://trtl.bz/2FEi9AW
  • [P2.T7] Hull’s EOC Question 15.6 on capital requirements for derivatives under original Basel  https://trtl.bz/2FCZSE2

Risk

Global Risks 2019

  • [GARP] Strategic Risk Will Be Front and Center in 2019 https://trtl.bz/2FDR3JY
  • [GARP] Term Versions of Libor-Replacement Rates Pick Up Steam https://trtl.bz/2FDJAus
  • Here’s Why Libor’s End Is a Headache for Switzerland https://trtl.bz/2FR9ri1 “Swiss franc-Libor rates underpin about $6.5 trillion of financial products and are used to price about 80% Swiss banks’ loans.
  • Allianz Risk Barometer 2019 https://trtl.bz/2FAagMA “Cyber risk is now a core concern for businesses in 2019 and beyond …

Risk Barometer 2019

Banking and Regulatory

minimum capital requirements

Quant, code and cyber

  • Detecting Credit Card Fraud Using Machine Learning (Catching Bad Guys with Data Science)  https://trtl.bz/2FOCbYz This is exactly one of Bart van Liebergen’s ML use cases (i.e., fraud) in the FRM P2.T9 assigned reading (Machine Learning: A Revolution in Risk Management and Compliance?)
  • An intuitive guide to Gaussian processes https://trtl.bz/2FDOUhl

Gaussian process

A Gentle Introduction to Exploratory Data Analysis

Case studies

  • Wells Fargo: Repairing a Damaged Brand (ft.com) https://trtl.bz/2FOgrMg (or pdf here  https://trtl.bz/2MkdMLC): “dozens describe a damaged brand, a workforce held back by fear of repeating past mistakes, and the immense difficulty of drawing a line under one of the ugliest banking scandals in an era full of them … A strong culture of credit risk management was matched by precious little culture of operational risk management.

wells fargo

euro crisis

Investing

O’Shaughnessy Quarterly Investor Letter

Other

 

 

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